cleanlab
token-label-error-benchmarks
| cleanlab | token-label-error-benchmarks | |
|---|---|---|
| 69 | 2 | |
| 8,673 | 3 | |
| 6.0% | - | |
| 9.4 | 10.0 | |
| 3 days ago | over 1 year ago | |
| Python | Jupyter Notebook | |
| GNU Affero General Public License v3.0 | GNU Affero General Public License v3.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cleanlab
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[Research] Detecting Annotation Errors in Semantic Segmentation Data
We have feely open-sourced our new method for improving segmentation data, published a paper on the research behind it, and released a 5-min code tutorial. You can also read more in the blog if you'd like.
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[R] Automated Quality Assurance for Object Detection Datasets
We’ve open-sourced one line of code to find errors in any object detection dataset via Cleanlab Object Detection, which can utilize any existing object detection model you’ve trained.
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[Research] Detecting Errors in Numerical Data via any Regression Model
If you'd like to learn more, you can check out the blogpost, research paper, code, and tutorial to run this on your data.
- Detecting Errors in Numerical Data via Any Regression Model
- cleanlab v2.5 now supports all major ML tasks (adds regression, object detection, and image segmentation)
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Automated Data Quality at Scale
Sharing some context here: in grad school, I spent months writing custom data analysis code and training ML models to find errors in large-scale datasets like ImageNet, work that eventually resulted in this paper (https://arxiv.org/abs/2103.14749) and demo (https://labelerrors.com/).
Since then, I’ve been interested in building tools to automate this sort of analysis. We’ve finally gotten to the point where a web app can do automatically in a couple of hours what I spent months doing in Jupyter notebooks back in 2019—2020. It was really neat to see the software we built automatically produce the same figures and tables that are in our papers.
The blog post shared here is results-focused, talking about some of the data and dataset-level issues that a tool using data-centric AI algorithms can automatically find in ImageNet, which we used as a case study. Happy to answer any questions about the post or data-centric AI in general here!
P.S. all of our core algorithms are open-source, in case any of you are interested in checking out the code: https://github.com/cleanlab/cleanlab
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Enhancing Product Analytics and E-commerce Business
Cleanlab Studio offers a user-friendly interface that allows you to visualize and review the identified issues in your dataset. You can easily explore the detected errors and make corrections with confidence. It's a hassle-free solution that can save you valuable time and improve your overall e-commerce operations. If you'd like more details you can check this article out.
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Databricks users can now automatically correct data and improve ML models
I thought this community might find it very useful that Databricks has partnered with Cleanlab to bring automated data correction and ML model improvement for both structured and unstructured datasets to all Databricks users.
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[R] Automated Checks for Violations of Independent and Identically Distributed (IID) Assumption
I just published a paper detailing this non-IID check and open-sourced its code in the cleanlab package — just one line of code will check for this and many other types of issues in your dataset.
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[P] Datalab: A Linter for ML Datasets
I recently published a blog introducing Datalab and an open-source Python implementation that is easy-to-use for all data types (image, text, tabular, audio, etc). For data scientists, I’ve made a quick Jupyter tutorial to run Datalab on your own data.
token-label-error-benchmarks
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New paper on Automatically Detecting Label Errors in Entity Recognition Data
Benchmarking code: https://github.com/cleanlab/token-label-error-benchmarks
Blogpost: https://cleanlab.ai/blog/entity-recognition/ Paper: https://arxiv.org/abs/2210.03920 Tutorial: https://docs.cleanlab.ai/stable/tutorials/token_classification.html Benchmarking code: https://github.com/cleanlab/token-label-error-benchmarks Source code: https://github.com/cleanlab/cleanlab Example entity recognition model: https://github.com/cleanlab/examples/blob/master/entity_recognition/entity_recognition_training.ipynb
What are some alternatives?
alibi-detect - Algorithms for outlier, adversarial and drift detection
examples - Notebooks demonstrating example applications of the cleanlab library
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
labelflow - The open platform for image labelling
karateclub - Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
SSL4MIS - Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
susi - SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
pigeonXT - 🐦 Quickly annotate data from the comfort of your Jupyter notebook
refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
snorkel - A system for quickly generating training data with weak supervision
AFFiNE - There can be more than Notion and Miro. AFFiNE(pronounced [ə‘fain]) is a next-gen knowledge base that brings planning, sorting and creating all together. Privacy first, open-source, customizable and ready to use.